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Large-scale dense mapping is vital in robotics, digital twins, and virtualreality. Recently, implicit neural mapping has shown remarkable reconstructionquality. However, incremental large-scale mapping with implicit neuralrepresentations remains problematic due to low efficiency, limited videomemory, and the catastrophic forgetting phenomenon. To counter thesechallenges, we introduce the Robot-centric Implicit Mapping (RIM) technique forlarge-scale incremental dense mapping. This method employs a hybridrepresentation, encoding shapes with implicit features via a multi-resolutionvoxel map and decoding signed distance fields through a shallow MLP. Weadvocate for a robot-centric local map to boost model training efficiency andcurb the catastrophic forgetting issue. A decoupled scalable global map isfurther developed to archive learned features for reuse and maintain constantvideo memory consumption. Validation experiments demonstrate our method'sexceptional quality, efficiency, and adaptability across diverse scales andscenes over advanced dense mapping methods using range sensors. Our system'scode will be accessible at \url{https://github.com/HITSZ-NRSL/RIM.git}.

Towards Large-Scale Incremental Dense Mapping using Robot-centric Implicit Neural Representation